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Retrieving Top-k Prestige-Based Relevant Spatial Web Objects
"... The location-aware keyword query returns ranked objects that are near a query location and that have textual descriptions that match query keywords. This query occurs inherently in many types of mobile and traditional web services and applications, e.g., Yellow Pages and Maps services. Previous work ..."
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Cited by 36 (7 self)
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The location-aware keyword query returns ranked objects that are near a query location and that have textual descriptions that match query keywords. This query occurs inherently in many types of mobile and traditional web services and applications, e.g., Yellow Pages and Maps services. Previous work considers the potential results of such a query as being independent when ranking them. However, a relevant result object with nearby objects that are also relevant to the query is likely to be preferable over a relevant object without relevant nearby objects. The paper proposes the concept of prestige-based relevance to capture both the textual relevance of an object to a query and the effects of nearby objects. Based on this, a new type of query, the Location-aware top-k Prestige-based Text retrieval (LkPT) query, is proposed that retrieves the top-k spatial web objects ranked according to both prestige-based relevance and location proximity. We propose two algorithms that compute LkPT queries. Empirical studies with real-world spatial data demonstrate that LkPT queries are more effective in retrieving web objects than a previous approach that does not consider the effects of nearby objects; and they show that the proposed algorithms are scalable and outperform a baseline approach significantly. 1.
The Power of Local Information in PageRank
"... Can one assess, by visiting only a small portion of a graph, if a given node has a significantly higher PageRank score than another? We show that the answer strongly depends on the interplay between the required correctness guarantees (is one willing to accept a small probability of error?) and the ..."
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Can one assess, by visiting only a small portion of a graph, if a given node has a significantly higher PageRank score than another? We show that the answer strongly depends on the interplay between the required correctness guarantees (is one willing to accept a small probability of error?) and the graph exploration model (can one only visit parents and children of already visited nodes?).
On the Embeddability of Random Walk Distances
"... Analysis of large graphs is critical to the ongoing growth of search engines and social networks. One class of queries centers around node affinity, often quantified by random-walk distances between node pairs, including hitting time, commute time, andpersonalized PageRank (PPR). Despite the potenti ..."
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Analysis of large graphs is critical to the ongoing growth of search engines and social networks. One class of queries centers around node affinity, often quantified by random-walk distances between node pairs, including hitting time, commute time, andpersonalized PageRank (PPR). Despite the potential of these “metrics, ” they are rarely, if ever, used in practice, largely due to extremely high computational costs. In this paper, we investigate methods to scalably and efficiently compute random-walk distances, by “embedding ” graphs and distances into points and distances in geometric coordinate spaces. We show that while existing graph coordinate systems (GCS) can accurately estimate shortest path distances, they produce significant errors when embedding random-walk distances. Based on our observations, we propose a new graph embedding system that explicitly accounts for per-node graph properties that affect random walk. Extensive experiments on a range of graphs show that our new approach can accurately estimate both symmetric and asymmetric random-walk distances. Once a graph is embedded, our system can answer queries between any two nodes in 8 microseconds, orders of magnitude faster than existing methods. Finally, we show that our system produces estimates that can replace ground truth in applications with minimal impact on application output. 1.
Reducing the history in decentralized interaction-based reputation systems
- in IFIP Networking
, 2012
"... Abstract. In decentralized interaction-based reputation systems, nodes store information about the past interactions of other nodes. Based on this information, they compute reputations in order to take decisions about future interactions. Computing the reputations with the complete history of intera ..."
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Abstract. In decentralized interaction-based reputation systems, nodes store information about the past interactions of other nodes. Based on this information, they compute reputations in order to take decisions about future interactions. Computing the reputations with the complete history of interactions is inefficient due to its resource requirements. Furthermore, the complete history of interactions accumulates old information, which may impede the nodes from capturing the dynamic behavior of the system when computing reputations. In this paper, we propose a scheme for reducing the amount of history maintained in decentralized interaction-based reputation systems based on such elements as the age of nodes, and we explore its effect on the computed reputations showing its effectiveness in both synthetic and real-world graphs.
Approximating pagerank locally with sublinear query complexity
- CoRR
"... The problem of approximating the PageRank score of a node with minimal information about the rest of the graph has attracted considerable attention in the last decade; but its central question, whether it is in general necessary to explore a non-vanishing fraction of the graph, remained open until n ..."
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The problem of approximating the PageRank score of a node with minimal information about the rest of the graph has attracted considerable attention in the last decade; but its central question, whether it is in general necessary to explore a non-vanishing fraction of the graph, remained open until now (only for specific graphs and/or nodes was a solution known). We present the first algorithm that produces a (1 ± )-approximation of the score of any one node in any n-node graph with probability (1 − ) visiting at most O(n 23 3√log(n)) = o(n) nodes. Our result is essentially tight (we prove that visiting Ω(n 2 3) nodes is in general necessary to solve even an easier “ranking ” version of the problem under any “natural ” graph exploration model, including all those in the literature) but it can be further improved for some classes of graphs and/or nodes of practical interest – e.g. to O(n 1 2 γ 1 2) nodes in graphs with maximum outdegree γ. ar X iv
Influence at Scale: Distributed Computation of Complex Contagion in Networks
"... We consider the task of evaluating the spread of influence in large networks in the well-studied independent cascade model. We describe a novel sampling approach that can be used to design scalable algorithms with provable perfor-mance guarantees. These algorithms can be implemented in distributed c ..."
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We consider the task of evaluating the spread of influence in large networks in the well-studied independent cascade model. We describe a novel sampling approach that can be used to design scalable algorithms with provable perfor-mance guarantees. These algorithms can be implemented in distributed computation frameworks such as MapReduce. We complement these results with a lower bound on the query complexity of influence estimation in this model. We validate the performance of these algorithms through exper-iments that demonstrate the efficacy of our methods and related heuristics. 1.
An integrated tag recommendation algorithm towards weibo user profiling
- In Proc. of DASFAA
, 2015
"... Abstract. In this paper, we propose a tag recommendation algorithm for profil-ing the users in Sina Weibo. Sina Weibo has become the largest and most popular Chinese microblogging system upon which many real applications are deployed such as personalized recommendation, precise marketing, customer r ..."
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Abstract. In this paper, we propose a tag recommendation algorithm for profil-ing the users in Sina Weibo. Sina Weibo has become the largest and most popular Chinese microblogging system upon which many real applications are deployed such as personalized recommendation, precise marketing, customer relationship management and etc. Although closely related, tagging users bears subtle differ-ence from traditional tagging Web objects due to the complexity and diversity of human characteristics. To this end, we design an integrated recommendation algorithm whose unique feature lies in its comprehensiveness by collectively ex-ploring the social relationships among users, the co-occurrence relationships and semantic relationships between tags. Thanks to deep comprehensiveness, our al-gorithm works particularly well against the two challenging problems of tradi-tional recommender systems, i.e., data sparsity and semantic redundancy. The extensive evaluation experiments validate our algorithm’s superiority over the state-of-the-art methods in terms of matching performance of the recommended tags. Moreover, our algorithm brings a broader perspective for accurately infer-ring missing characteristics of user profiles in social networks.
Semantic-based Recommendation across Heterogeneous Domains
"... Cross-domain recommendation has attracted wide research interest which generally aims at improving the recommendation performance by alleviating the cold start problem in collaborative filtering based recommendation or generating a more comprehensive user profiles from multiple domains. In most pre ..."
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Cross-domain recommendation has attracted wide research interest which generally aims at improving the recommendation performance by alleviating the cold start problem in collaborative filtering based recommendation or generating a more comprehensive user profiles from multiple domains. In most previous cross-domain recommendation settings, explicit or implicit relationships can be easily established across different domains. However, many real applications belong to a more challenging setting: recommendation across heterogeneous domains without explicit relationships, where neither explicit user-item relations nor overlapping features exist between different domains. In this new setting, we need to (1) enrich the sparse data to characterize users or items and (2) bridge the gap caused by the heterogenous features in different domains. To overcome the first challenge, we proposed an optimized local tag propagation algorithm to generate descriptive tags for user profiling. For the second challenge, we proposed a semantic relatedness metric by mapping the heterogenous features onto their concept space derived from online encyclopedias. We conducted extensive experiments on two real datasets to justify the effectiveness of our solution.
, and
, 2015
"... A local PageRank algorithm for evaluating the importance of scientific articles ..."
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A local PageRank algorithm for evaluating the importance of scientific articles
Local Ranking Problem on the BrowseGraph
"... The “Local Ranking Problem ” (LRP) is related to the com-putation of a centrality-like rank on a local graph, where the scores of the nodes could significantly di↵er from the ones computed on the global graph. Previous work has studied LRP on the hyperlink graph but never on the BrowseGra-ph, namely ..."
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The “Local Ranking Problem ” (LRP) is related to the com-putation of a centrality-like rank on a local graph, where the scores of the nodes could significantly di↵er from the ones computed on the global graph. Previous work has studied LRP on the hyperlink graph but never on the BrowseGra-ph, namely a graph where nodes are webpages and edges are browsing transitions. Recently, this graph has received more and more attention in many di↵erent tasks such as ranking, prediction and recommendation. However, a web-server has only the browsing trac performed on its pages (local BrowseGraph) and, as a consequence, the local com-putation can lead to estimation errors, which hinders the increasing number of applications in the state of the art. Also, although the divergence between the local and global ranks has been measured, the possibility of estimating such divergence using only local knowledge has been mainly over-looked. These aspects are of great interest for online service providers who want to: (i) gauge their ability to correctly assess the importance of their resources only based on their local knowledge, and (ii) take into account real user brow-sing fluxes that better capture the actual user interest than the static hyperlink network. We study the LRP problem on a BrowseGraph from a large news provider, considering as subgraphs the aggregations of browsing traces of users coming from di↵erent domains. We show that the distan-ce between rankings can be accurately predicted based only on structural information of the local graph, being able to achieve an average rank correlation as high as 0.8.
